Comparer des méthodes

	Validation croisée	Simulation de Monte-Carlo	Test par permutation (ou randomisation)
Domaine	Prise de décision	Prise de décision	Statistique
Famille	MCDM	MCDM	Regression model
Année d'origine	1974	1949	2005
Auteur d'origine	Stone, M.	Metropolis, N., Ulam, S.	Good (2005); Edgington & Onghena (2007); resampling tradition
Type	Robustness wrapper — k-fold cross-validation for MCDM stability	Robustness wrapper — Monte Carlo uncertainty propagation	Nonparametric resampling test
Source fondatrice	Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society Series B	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Alias	—	—	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Apparentées	0	0	5
Résumé	CROSS-VALIDATION (Cross-Validation — k-fold hold-out validation of MCDM decision consistency) is a ranking multi-criteria decision-making (MCDM) method introduced by Stone, M. in 1974. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.